Variance Reduction in Low Light Image Enhancement Model
V.deepika1, C. Nivedha2, P.S. Sai roshini3, Guide: S. Arun Kumar4
1V.Deepika, Computer science, SRM Institute of science and technology, Chennai, India.
2C.Nivedha, Computer science, SRM Institute of science and technology, Chennai, India.
3Sai Roshini.P. S, Computer science, SRM Institute of science and technology, Chennai, India.
4Guide: S. Arun Kumar Assistant Professor Department of Computer Science Engineering SRM Institute of Science & Technology Chennai, India.
Manuscript received on October 06, 2020. | Revised Manuscript received on October 25, 2020. | Manuscript published on November 30, 2020. | PP: 139-142 | Volume-9 Issue-4, November 2020. | Retrieval Number: 100.1/ijrte.D4723119420 | DOI: 10.35940/ijrte.D4723.119420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In image processing, enhancement of images taken in low light is considered to be a tricky and intricate process, especially for the images captured at nighttime. It is because various factors of the image such as contrast, sharpness and color coordination should be handled simultaneously and effectively. To reduce the blurs or noises on the low-light images, many papers have contributed by proposing different techniques. One such technique addresses this problem using a pipeline neural network. Due to some irregularity in the working of the pipeline neural networks model [1], a hidden layer is added to the model which results in a decrease in irregularity.
Keywords: Image enhancement, Machine learning, Neural network, Pipeline.